Comparison of Spatial Interpolation Methods for Estimating the Annual Rainfall in the Wadi Al-Mujib Basin in Jordan

Abstract

Accurate rainfall data are essential for environmental applications in the actual assessment of the geographical distribution of rainfall. Interpolation methods are usually applied to monitor the spatial distribution of the rainfall data. There are many spatial interpolation methods, but none of them can achieve in all cases the best results. In this study, three different interpolation methods were investigated with regard to their suitability for producing a spatial rainfall distribution. Rainfall data from 14  meteorological stations were spatially interpolated using three common interpolation techniques: inverse distance weighting (IDW), ordinary kriging (OK), and kernel smoothing (KS) were compared and assessed against station rainfall data and modeled rainfall. Cross-validation was applied to evaluate the accuracy of interpolation methods in terms of the root-mean-square error (RMSE). The best results were obtained by the lowest RMSE for interpolating the precipitation (RMSE) = 100.86542, while the inverse distance weighting (IDW) performed the worst, and are least efficient with the largest RMSE=103.43; in addition, the kernel smoothing with the least minimum (-) and maximum (+) error is
-92.38 mm and 313.33 mm.